Anomaly Detection with Foundation Models

(ADFM)

In Conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026

Colorado Convention Center, Denver, CO, USA

June 3 - 4, 2026

About ADFM 2026

About ADFM 2026

The rapid advancement of foundation models in fields like healthcare, cybersecurity, and finance highlights the urgent need to improve their anomaly detection capabilities. Despite their growing application in high-stakes areas, the challenges of using these models for anomaly detection remain underexplored. The Anomaly Detection with Foundation Models (ADFM 2026) workshop aims to address this gap by focusing on the intersection of foundation models and anomaly detection. Our organizing and technical committee, composed of leading experts, provides a platform for advancing research and discussing the recent breakthroughs, and the technical and ethical implications of deploying these models. ADFM 2026 will foster interdisciplinary collaboration and contribute to the development of more reliable and effective anomaly detection systems in artificial intelligence.

Where

Colorado Convention Center, Denver, CO, USA

When

June 3 - 4, 2026

Keynote Speakers

[More info about keynote speakers will be updated here]

Lizhuang Ma

Lizhuang Ma

Shanghai Jiao Tong U. & East China Normal U.

Confirmed

Danijel Skočaj

Danijel Skočaj

University of Ljubljana

Confirmed

Yu Zhou

Yu Zhou

Huazhong University of Science and Technology

Confirmed

Bin-Bin Gao

Bin-Bin Gao

Tencent

Confirmed

ADFM 2026 Schedule

[in Denver local time]

[More info about the schedule will be updated here]



Submissions

Submission Instructions

We welcome full paper submissions. The papers must be no longer than 8 pages in total (excluding references). Please submit at the following CMT website:
ADFM 2026 CMT submission website.

  • All submissions are handled via the workshop’s CMT website.
  • Submissions should be made in PDF format and should follow the official CVPR 2026 template and guidelines.
  • All submissions must be anonymous and conform to the CVPR 2026 conference guidelines for double-blind review.
  • ADFM 2026 workshop follows the CVPR 2026 conference guidelines and does not allow dual submissions.
  • Authors may upload optional supplementary materials, containing additional details, videos, images, etc. in a separate zip file (with a max of 50MB in size); the deadline for submitting these supplementary materials is the same as that for the main paper.
  • The accepted papers will be presented as either an oral, spotlight, or poster presentation.
  • By submitting a paper to the ADFM 2026 workshop for review, the authors must agree that at least one author or authors' representative of each accepted submission must present the paper at the workshop in-person and that they are willing and able to serve as the reviewers of the ADFM 2026 workshop submissions if needed (decided by the ADFM 2026 workshop organizing team).
  • The presentation of accepted papers at our workshop will follow the same policy as that for the accepted papers at the CVPR 2026 main conference.
  • The accepted papers will be made publicly accessible on the workshop website shortly after the camera-ready deadline. CVPR 2026 will provide the official proceedings of the accepted papers.
  • Failure to comply with the aforesaid rules may cause the paper to be removed from the workshop program.
Submission deadline:       February 27, 2026 11:59 PM EDT  
Notification to authors:    March 20, 2026
Camera ready deadline:   April 10, 2026

We invite the submission of original and high-quality research papers in the topics related to anomaly detection with foundation models.

We're seeking dedicated Reviewers! Please self-nominate via the reviewer self-nomination form. Thanks for your support!


Topics

The topics for ADFM 2026 include, but are not limited to:

  • Fundamental theories and principles of foundation models for anomaly detection.
  • Advanced anomaly detection algorithms and frameworks utilizing foundation models.
  • Sector-specific anomaly detection employing foundation models, covering areas such as finance, healthcare, cybersecurity, and industrial systems.
  • Evaluation standards and benchmarks for appraising anomaly detection in foundation models.
  • Methods enhancing the clarity and comprehensibility of foundation models in anomaly detection.
  • Methods promoting fairness and diminishing bias in anomaly detection with foundation models.
  • Privacy-enhancing techniques in anomaly detection with foundation models.
  • Trust and reliability of foundation models in crucial anomaly detection applications.
  • Interdisciplinary methods for refining anomaly detection, incorporating insights from fields like psychology and sociology.
  • Adaptive learning and adjustment mechanisms for foundation models in dynamic settings.
  • Integration of expert knowledge and domain-specific systems with foundation models for enhanced anomaly detection.
  • Exploratory discussions on the constraints and challenges of current foundation models in identifying anomalies in complex and noisy datasets.
  • Prospective insights on the evolution of anomaly detection methods with the advancement of foundation models.
 



ADFM 2026 Venue

Colorado Convention Center, Denver, CO, USA

ADFM 2026 will be held at the Colorado Convention Center, Denver, CO, USA on June 3 - 4, 2026.

Organizers

Kuan-Chuan Peng

Kuan-Chuan Peng

Mitsubishi Electric Research Laboratories (MERL)

Ying Zhao

Ying Zhao

Ricoh Software Research Center (Beijing) Co.,Ltd.

Abhishek Aich

Abhishek Aich

NEC Laboratories, America

Steering Committee

Xian Tao

Xian Tao

Chinese Academy of Sciences

Wenbing Zhu

Wenbing Zhu

Fudan University and Rongcheer